An interactive Tableau dashboard that visualizes key insights into data science job salaries, leveraging data points like experience level, job type, company size, and geographic distribution. This dashboard serves as an exploration tool for understanding trends and patterns in compensation across the global data science industry.
The Data Science Job Salaries Analysis Dashboard is built to provide recruiters, job seekers, and industry analysts with insights into how data science roles are compensated based on experience, location, and other critical job attributes. It combines various visualization techniques to allow deep dives into salary distributions, top regions for data science employment, and preferred experience and job types.
- Analyze company demographics by size and location.
- Explore salary ranges by experience level and job type.
- Identify top countries where data science roles are most prevalent.
- Compare average salaries across job roles and regions.
This project utilizes a dataset from Kaggle, containing real-world data on data science job salaries.
- Dataset Source: Kaggle - DS Salaries Dataset
- File Used:
ds_salaries.csv
- ID: Unique identifier for each role
- Work Year: Year of the salary data
- Experience Level: Levels include Entry (EN), Intermediate (MI), Senior (SE), and Expert (EX)
- Employment Type: Types include Full-Time (FT), Part-Time (PT), Contract (CT), and Freelance (FL)
- Job Title: Specific title of the data role
- Salary: The total gross salary amount paid
- Salary Currency: The currency of the salary paid as an ISO 4217 currency code
- Salary in USD: Annual salary, normalized to USD
- Employee Residence: Employee's residing country
- Remote Ratio: The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)
- Company Location: The country of the employer's main office or contracting branch as an ISO 3166 country code
- Company Size: Categorical representation of company size: Large (L), Medium (M), Small (S)
- Tableau: Tableau Desktop or Tableau Public (required to open or modify the dashboard).
- Dataset: Download the dataset
- Data Preparation:
- Import
ds_salaries.csvinto Tableau. - Assign aliases to categorical values (e.g., EN -> Entry, MI -> Intermediate).
- Import
- Visualizations:
- Create charts as described in the Dashboard Components section.
- Use calculated fields to convert salaries to standardized ranges if needed.
- Formatting:
- Customize color schemes, fonts, and layouts to improve readability and aesthetics.
- Filters and Interactivity:
- Add dynamic filters for Country, Job Title, and Company Size to allow detailed data exploration.
- Experience Matters: Higher experience levels command significantly higher average salaries across most locations.
- Geographical Trends: Certain regions, especially in North America and Western Europe, dominate in terms of data science salaries and job prevalence.
- Remote Work Prevalence: The data suggests that remote roles are increasingly popular in the data science field, with a sizable proportion of jobs offering remote flexibility.
- Which countries offer the highest average salaries for data scientists?
- What’s the salary gap between entry-level and senior-level data science roles?
- How does employment type affect salary distribution?
- Trend Analysis: Incorporate year-over-year changes in salaries and remote work ratios.
- Job Role Breakdown: Add more detailed visualizations based on specific data science roles (e.g., Data Engineer, ML Engineer).
- Regional Cost of Living Adjustment: Adjust salaries based on regional living costs to reflect real earnings.
- Tableau Desktop/Public: For building and visualizing the dashboard.
Special thanks to Kaggle for the dataset and the Tableau Community for inspiration and resources that assisted in building this dashboard.